Phase 21: Intent-first pipeline, visible ack, KV-stable system prompt

Pipeline changes (generation_task.py, intent.py):
- Remove optimistic streaming queue/race (_drain_queue deleted)
- Remove _generate_acknowledgment — ack now embedded in intent JSON
- Round 0: await intent (~400ms), stream ack immediately as TTFT,
  then execute tool sequentially; chat-only streams directly
- IntentResult.ack: one-sentence acknowledgment, intent max_tokens 200→350
- _parse_intent extracts and trims ack field

KV cache stability (llm.py, generation_buffer.py, generation_task.py):
- build_context: replace cached_note_ids with include_note_ids
- Auto-found notes populate context_meta["auto_notes"] for sidebar but
  are NOT injected into system prompt (--- Related Notes --- removed)
- Explicitly included notes injected as --- Included Notes ---
- _conv_note_cache dict + get/set/clear functions removed from generation_buffer.py
- All clear_conv_note_cache() calls removed

Cold model retry (llm.py):
- generate_completion (used by classify_intent) retries on HTTP 500:
  3 attempts with 3s/6s delays — prevents intent failure during cold load

API + frontend (routes/chat.py, stores/chat.ts, views/ChatView.vue, components/ChatPanel.vue):
- exclude_note_ids → include_note_ids throughout
- ChatView sidebar: Suggested (auto-found, + to include) + In Context (× to remove)
- ChatPanel: remove exclude button from context pills; no IDs passed to sendMessage

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/fabledassistant/services/intent.py

@@ -22,6 +22,7 @@ class IntentResult:
     tool_name: str | None = None  # None = no tool, just chat
     arguments: dict = field(default_factory=dict)
     confidence: str = "high"  # "high", "medium", or "low"
+    ack: str | None = None  # One-sentence acknowledgment to stream immediately
 
     @property
     def should_execute(self) -> bool:
@@ -61,8 +62,8 @@ Available tools:
 {tool_summary}
 
 Respond with ONLY a JSON object, no other text:
-- If a tool should be called: {{"tool": "tool_name", "arguments": {{...}}, "confidence": "high"|"medium"|"low"}}
-- If it's general chat: {{"tool": null, "confidence": "high"}}
+- If a tool should be called: {{"tool": "tool_name", "arguments": {{...}}, "confidence": "high"|"medium"|"low", "ack": "One short sentence describing what you're about to do."}}
+- If it's general chat: {{"tool": null, "confidence": "high", "ack": null}}
 
 Confidence levels:
 - "high": the intent is clear and all required arguments are unambiguous
@@ -99,6 +100,7 @@ Rules:
 - "read", "open", "show me", "what does X say", "display", "pull up" a specific note → use get_note with query=<note name>.
 - "list my notes", "show notes", "recent notes", "browse notes", "notes tagged X" → use list_notes (with optional q or tags).
 - "tag X with Y", "add tag Y to X", "untag Y from X", "remove tag Y from X" → use update_note with tags=[Y] and tag_mode="add" or "remove".
+- "ack": one short, natural sentence confirming the action (tool path only). Vary phrasing — do not always start with "Let me". Omit (null) for chat-only responses.
 - Do NOT wrap the JSON in markdown code fences."""
 
 
@@ -142,7 +144,7 @@ async def classify_intent(
     messages.append({"role": "user", "content": user_message})
 
     try:
-        raw = await generate_completion(messages, model, max_tokens=200)
+        raw = await generate_completion(messages, model, max_tokens=350)
     except Exception:
         logger.warning("Intent classification LLM call failed", exc_info=True)
         return IntentResult()
@@ -192,11 +194,15 @@ def _parse_intent(raw: str, tools: list[dict]) -> IntentResult:
     if not isinstance(arguments, dict):
         arguments = {}
 
+    ack = parsed.get("ack") or None
+    if ack is not None:
+        ack = ack.strip() or None
+
     logger.info(
         "Intent classified: tool=%s, confidence=%s, args=%s",
         tool_name, confidence, json.dumps(arguments)[:200],
     )
-    return IntentResult(tool_name=tool_name, arguments=arguments, confidence=confidence)
+    return IntentResult(tool_name=tool_name, arguments=arguments, confidence=confidence, ack=ack)
 
 
 def _try_json(text: str) -> dict | list | None: